Articulating the Trauma‐Informed Theory of Individual Health Behavior
Bibliographic record
Abstract
Exposure to trauma increases the risk of engaging in detrimental health behaviours such as tobacco and substance use. In response, the United States Substance Abuse and Mental Health Services Administration developed Trauma-Informed Care (TIC), an organisational framework for improving the provision of behavioural health care to account for the role exposure to trauma plays in patients' lives. We adapt TIC to introduce a novel theory of behaviour change, the Trauma-Informed Theory of Individual Health Behavior (TTB). TTB posits that individual capacity to undertake intentional health-promoting behaviour change is dependent on three factors: (1) the forms and severity of trauma they have been and are exposed to, (2) how this trauma physiologically manifests (i.e., the trauma response) and (3) resilience to undertake behaviour change despite this trauma response. We define each of these factors and their relationships to one another. We anticipate that the introduction of TTB will provide a foundation for developing theory-driven research, interventions, and policies that improve behavioural health outcomes in trauma-affected populations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.014 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".